I’m Marko, CTO at Memgraph. This post was written by my colleague Matt, and I can help answer questions about the migration and the reasoning behind it.
The post covers why we moved parts of our infrastructure to Hetzner, including cost, operational overhead, and performance consistency. One of the main takeaways for us was that dedicated infrastructure gave us more predictable benchmarking and reduced some of the maintenance burden we previously had with a colocated setup.
Happy to answer questions about the tradeoffs, what worked well, and what we’d do differently.
Hi all! I'm Marko, CTO at Memgraph, and the author of this post.
The post argues that the GraphRAG pipeline can be expressed as a single database query rather than a chain of application-layer steps. The idea is to keep components such as retrieval, expansion, ranking, and final context assembly within the database query plan.
I go through:
* what I mean by GraphRAG in practice;
* why "single-query" execution can reduce moving parts;
* why can that help with latency/cost by returning only the final payload;
* and how it can make tracing/debugging easier by returning the context plus the path used to assemble it.
The post also contrasts this with Python-orchestrated pipelines and touches on agentic pipeline selection (called Agentic GraphRAG). Happy to answer technical questions or discuss where this breaks down.
Hi all - I’m Marko, CTO at Memgraph. The author of this post, David, also works at Memgraph.
This post explains how our vector index is implemented within the same storage engine as the graph (eliminating the need for a separate vector store), how we avoid double vector storage, and how scalar type choices (f32/f16/etc) affect memory usage. It also covers some implementation details (USearch-backed index, concurrency, and recovery behavior).
We included a benchmark on 1M nodes with 1024-dim embeddings comparing versions 3.7.2 and 3.8.0, and saw large RAM reductions in the newer version while keeping load and response times similar. Happy to answer technical questions.
Agree with the measures; follow-up question: what's the insight definition? I think exposing some of those measures would help people better understand what the analysis covered, in other words, how much data was actually analyzed. Maybe an additional measure is some kind of breadth (I guess it could be derived from the throughput).
"Informational leverage" reminded me of "retrieval leverage" because yeah, the scale of data didn't change, the ability to extract insights did :D
The post covers why we moved parts of our infrastructure to Hetzner, including cost, operational overhead, and performance consistency. One of the main takeaways for us was that dedicated infrastructure gave us more predictable benchmarking and reduced some of the maintenance burden we previously had with a colocated setup.
Happy to answer questions about the tradeoffs, what worked well, and what we’d do differently.